Department of Gastrointestinal Surgery, The Fifth Affiliated Hospital of Sun Yat- Sen University, No.52 Mei Hua East Road, Xiang Zhou District, Zhuhai, Guangdong, 519000, China.
Department of General Surgery, The First People's Hospital of Kashagr, Xinjiang Uygur Autonomous Region, Kashagr Region, 844000, China.
BMC Gastroenterol. 2024 Oct 9;24(1):355. doi: 10.1186/s12876-024-03445-y.
Gangrene and perforation are severe complications of acute appendicitis, associated with a higher mortality rate compared to uncomplicated appendicitis. Accurate preoperative identification of Gangrenous or perforated appendicitis (GPA) is crucial for timely surgical intervention.
This retrospective multicenter study includes 796 patients who underwent appendectomy. Univariate and multivariate logistic regression analyses are used to develop a nomogram model for predicting GPA based on laboratory tests and computed tomography (CT) findings. The model is validated using an external dataset.
Seven independent predictors were included in the nomogram: white blood cell count, lymphocyte count, D-dimer, serum glucose, albumin, maximum outer diameter of the appendix, and presence of appendiceal fecalith. The nomogram achieved good discrimination and calibration in both the training and testing sets. In the training set, the AUC was 0.806 (95%CI: 0.763-0.849), and the sensitivity and specificity were 82.1% and 66.9%, respectively. The Hosmer-Lemeshow test showed good calibration (P = 0.7378). In the testing set, the AUC was 0.799 (95%CI: 0.741-0.856), and the sensitivity and specificity were 70.5% and 75.3%, respectively. Decision curve analysis (DCA) confirmed the clinical utility of the nomogram.
The laboratory test-CT nomogram model can effectively identify GPA patients, aiding in surgical decision-making and improving patient outcomes.
坏疽和穿孔是急性阑尾炎的严重并发症，与单纯性阑尾炎相比，其死亡率更高。准确预测坏疽性或穿孔性阑尾炎（GPA）对于及时进行手术干预至关重要。
本回顾性多中心研究纳入 796 例行阑尾切除术的患者。采用单变量和多变量逻辑回归分析，基于实验室检查和计算机断层扫描（CT）结果，建立预测 GPA 的列线图模型。使用外部数据集对模型进行验证。
纳入列线图的 7 个独立预测因子包括：白细胞计数、淋巴细胞计数、D-二聚体、血清葡萄糖、白蛋白、阑尾最大外径和阑尾粪石。该列线图在训练集和测试集中均具有良好的区分度和校准度。在训练集中，AUC 为 0.806（95%CI：0.763-0.849），灵敏度和特异度分别为 82.1%和 66.9%。Hosmer-Lemeshow 检验显示校准良好（P=0.7378）。在测试集中，AUC 为 0.799（95%CI：0.741-0.856），灵敏度和特异度分别为 70.5%和 75.3%。决策曲线分析（DCA）证实了该列线图的临床实用性。
实验室检查-CT 列线图模型可有效识别 GPA 患者，有助于手术决策，并改善患者预后。
